Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment
Authors: Teng Xiao, Yige Yuan, Huaisheng Zhu, Mingxiao Li, Vasant Honavar
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results of our experiments on a variety of standard benchmarks show that Cal-DPO remarkably improves off-the-shelf methods. |
| Researcher Affiliation | Collaboration | Teng Xiao1, Yige Yuan2, Huaisheng Zhu1, Mingxiao Li3, Vasant G Honavar1 1Artificial Intelligence Research Laboratory, Pennsylvania State University 2University of Chinese Academy of Sciences, 3Tencent AI Lab EMAIL EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: A Pytorch-style Pseudocode of Cal-DPO |
| Open Source Code | Yes | Code is available at https://github.com/tengxiao1/Cal-DPO. |
| Open Datasets | Yes | We evaluate Cal-DPO on four widely used datasets for preference fine-tuning: the Ultra Feedback Binarized dataset [53, 54], Reddit TL;DR summarization dataset [14], Anthropic-HH dataset [1], and the IMDb sentiment dataset [13]. https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized, https://huggingface.co/datasets/Anthropic/hh-rlhf, https://huggingface.co/datasets/openai/summarize_from_feedback, https://huggingface.co/datasets/stanfordnlp/imdb |
| Dataset Splits | Yes | IMDb Sentiment [13]: This dataset 4 contains movie reviews from the IMDb with positive and negative sentiment, which contains 25k training samples and each 5k samples for validation and test. |
| Hardware Specification | Yes | The experiments on are run on 4 Nvidia A100 GPUs with BF16 precision. |
| Software Dependencies | No | The paper mentions 'Pytorch-style Pseudocode' but does not specify version numbers for PyTorch or any other software libraries or dependencies. |
| Experiment Setup | Yes | The β of Cal-DPO is searched from [1e-3, 2e-3, 3e-3, 1e-2, 1e-1], the batch size for all methods is 128, and we use the RMSprop optimizer with a learning rate of 5e-6. We linearly warm up the learning rate from 0 to 5e-6 in 150 steps. The sampling temperature is set to 1 for all experiments. |